Individual glacier analysis 1
Contents
Individual glacier analysis 1¶
This notebook will walk you through steps to read in and organize velocity data and clip it to the extent of a single glacier. The tools we will use include xarray, rioxarray and geopandas.
To clip its_live data to the extent of a single glacier we will use a vector dataset of glacier outlines, the Randolph Glacier Inventory. These aren’t cloud-hosted currently so you will need to download the data to your local machine.
Learning goals come back and finish these, feel like this notebook has alot, is pretty disorganized.. using xarray to read zarr data from s3 bucket
rio.clip()to clip raster by vectorviewing CRS, reprojecting and writing CRS data for various objects
dataset.where()
dataset.sel() using multiple conditions
groupby
First, lets install the python libraries that were listed on the Software page:
import geopandas as gpd
import os
import numpy as np
import xarray as xr
import rioxarray as rxr
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from shapely.geometry import Polygon
from shapely.geometry import Point
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy
import cartopy.feature as cfeature
import json
import urllib.request
from skimage.morphology import skeletonize
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
%config InlineBackend.figure_format='retina'
Reading in ITS_LIVE data¶
We will use some of the functions we defiend in the data access notebook to read in data here. First, let’s read in the catalog again:
#import itslivetools
with urllib.request.urlopen('https://its-live-data.s3.amazonaws.com/datacubes/catalog_v02.json') as url_catalog:
itslive_catalog = json.loads(url_catalog.read().decode())
itslive_catalog.keys()
dict_keys(['type', 'features'])
Take a look at a single catalog entry:
Use the function below to find the url that corresponds to the zarr datacube for a specific point:
def find_granule_by_point(input_dict, input_point): #[lon,lat]
'''Takes an inputu dictionary (a geojson catalog) and a point to represent AOI.
this returns a list of the s3 urls corresponding to zarr datacubes whose footprint covers the AOI'''
#print([input_points][0])
target_granule_urls = []
#Point(coord[0], coord[1])
#print(input_point[0])
#print(input_point[1])
point_geom = Point(input_point[0], input_point[1])
#print(point_geom)
point_gdf = gpd.GeoDataFrame(crs='epsg:4326', geometry = [point_geom])
for granule in range(len(input_dict['features'])):
#print('tick')
bbox_ls = input_dict['features'][granule]['geometry']['coordinates'][0]
bbox_geom = Polygon(bbox_ls)
bbox_gdf = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry = [bbox_geom])
#if poly_gdf.contains(points1_ls[poly]).all() == True:
if bbox_gdf.contains(point_gdf).all() == True:
#print('yes')
target_granule_urls.append(input_dict['features'][granule]['properties']['zarr_url'])
else:
pass
#print('no')
return target_granule_urls
This function will read in a xarray dataset from a url to a zarr datacube when we’re ready:
I started with chunk_size='auto' but ran into issues. more about choosing good chunk sizes here.
def read_in_s3(http_url):
s3_url = http_url.replace('http','s3')
s3_url = s3_url.replace('.s3.amazonaws.com','')
datacube = xr.open_dataset(s3_url, engine = 'zarr',
storage_options={'anon':True},
chunks = 'auto')
return datacube
def get_bbox_single(input_xr):
'''Takes input xr object (from itslive data cube), plots a quick map of the footprint.
currently only working for granules in crs epsg 32645'''
xmin = input_xr.coords['x'].data.min()
xmax = input_xr.coords['x'].data.max()
ymin = input_xr.coords['y'].data.min()
ymax = input_xr.coords['y'].data.max()
pts_ls = [(xmin, ymin), (xmax, ymin),(xmax, ymax), (xmin, ymax), (xmin, ymin)]
#print(input_xr.mapping.spatial_epsg)
#print(f"epsg:{input_xr.mapping.spatial_epsg}")
crs = f"epsg:{input_xr.mapping.spatial_epsg}"
#crs = {'init':f'epsg:{input_xr.mapping.spatial_epsg}'}
#crs = 'epsg:32645'
#print(crs)
polygon_geom = Polygon(pts_ls)
polygon = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[polygon_geom])
#polygon = polygon.to_crs('epsg:4326')
bounds = polygon.total_bounds
return polygon
url = find_granule_by_point(itslive_catalog, [84.56, 28.54])
url
['http://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr']
dc = read_in_s3(url[0])
dc
<xarray.Dataset>
Dimensions: (mid_date: 20549, y: 833, x: 833)
Coordinates:
* mid_date (mid_date) datetime64[ns] 2020-02-07T17:10:52....
* x (x) float64 2.001e+05 2.002e+05 ... 2.999e+05
* y (y) float64 3.2e+06 3.2e+06 ... 3.1e+06 3.1e+06
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(20549,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
... ...
vy_error_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_modeled (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 20549
- y: 833
- x: 833
- mid_date(mid_date)datetime64[ns]2020-02-07T17:10:52.528083968 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2020-02-07T17:10:52.528083968', '2021-11-11T05:10:40.529083904', '2015-03-18T00:19:03.524224768', ..., '2018-12-10T04:47:43.028084992', '2016-07-05T04:47:51.028084992', '2019-06-11T04:54:02.028084224'], dtype='datetime64[ns]') - x(x)float642.001e+05 2.002e+05 ... 2.999e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
array([200092.5, 200212.5, 200332.5, ..., 299692.5, 299812.5, 299932.5])
- y(y)float643.2e+06 3.2e+06 ... 3.1e+06 3.1e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
array([3199867.5, 3199747.5, 3199627.5, ..., 3100267.5, 3100147.5, 3100027.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 401.35 kiB 401.35 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 18.42 MiB 18.42 MiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - mapping()<U1...
- CoordinateAxisTypes :
- GeoX GeoY
- CoordinateTransformType :
- Projection
- GeoTransform :
- 200032.5 120.0 0 3199927.5 0 -120.0
- grid_mapping_name :
- universal_transverse_mercator
- inverse_flattening :
- 298.257223563
- semi_major_axis :
- 6378137.0
- spatial_epsg :
- 32645
- spatial_proj4 :
- +proj=utm +zone=45 +datum=WGS84 +units=m +no_defs
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- utm_zone_number :
- 45.0
array('', dtype='<U1') - mission_img1(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 442 Tasks 441 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
We are reading this in as a dask array. Let’s take a look at the chunk sizes:
NOTE: chunksizes shows the largest chunk size. chunks shows the sizes of all chunks along all dims, better if you have irregular chunks
dc.chunksizes
Frozen({'mid_date': (20549,), 'y': (40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 33), 'x': (40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 33)})
dc.chunks
Frozen({'mid_date': (20549,), 'y': (40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 33), 'x': (40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 33)})
I think it could be useful to talk about dask chunk sizes here? Especially since I run into a warning a few steps down. Need to look into rechunking more to better undrestand first – fix warning below but still not sure about specifying chunk sizes
Check CRS of xr object:
dc.mapping
<xarray.DataArray 'mapping' ()>
array('', dtype='<U1')
Attributes:
CoordinateAxisTypes: GeoX GeoY
CoordinateTransformType: Projection
GeoTransform: 200032.5 120.0 0 3199927.5 0 -120.0
grid_mapping_name: universal_transverse_mercator
inverse_flattening: 298.257223563
semi_major_axis: 6378137.0
spatial_epsg: 32645
spatial_proj4: +proj=utm +zone=45 +datum=WGS84 +units=m +no_defs
spatial_ref: PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",...
utm_zone_number: 45.0- ...
array('', dtype='<U1') - CoordinateAxisTypes :
- GeoX GeoY
- CoordinateTransformType :
- Projection
- GeoTransform :
- 200032.5 120.0 0 3199927.5 0 -120.0
- grid_mapping_name :
- universal_transverse_mercator
- inverse_flattening :
- 298.257223563
- semi_major_axis :
- 6378137.0
- spatial_epsg :
- 32645
- spatial_proj4 :
- +proj=utm +zone=45 +datum=WGS84 +units=m +no_defs
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- utm_zone_number :
- 45.0
Let’s take a look at the time dimension (mid_date here). To start with we’ll just print the first 10 values:
for element in range(10):
print(dc.mid_date[element].data)
2020-02-07T17:10:52.528083968
2021-11-11T05:10:40.529083904
2015-03-18T00:19:03.524224768
2019-10-10T17:13:32.528083968
2016-07-15T05:06:36.529083904
2021-08-10T17:10:34.529083904
2021-04-15T00:19:35.894403072
2018-06-27T17:09:39.528083968
2019-12-22T05:10:48.528083968
2020-07-29T05:10:51.528083968
Weird, it doesn’t look like the time dimension is in chronological order, let’s fix that:
dc_timesorted = dc.sortby(dc['mid_date'])
dc_timesorted
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
/Users/emarshall/miniconda3/envs/mynewbook/lib/python3.10/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large
chunk and silence this warning, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
... array[indexer]
To avoid creating the large chunks, set the option
>>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
... array[indexer]
return self.array[key]
<xarray.Dataset>
Dimensions: (mid_date: 20549, y: 833, x: 833)
Coordinates:
* mid_date (mid_date) datetime64[ns] 2013-04-09T16:49:00....
* x (x) float64 2.001e+05 2.002e+05 ... 2.999e+05
* y (y) float64 3.2e+06 3.2e+06 ... 3.1e+06 3.1e+06
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(20549,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
... ...
vy_error_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_modeled (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 20549
- y: 833
- x: 833
- mid_date(mid_date)datetime64[ns]2013-04-09T16:49:00.528084992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2013-04-09T16:49:00.528084992', '2013-04-23T04:55:55.528083968', '2013-04-25T16:49:09.528084992', ..., '2022-04-20T05:10:57.028083968', '2022-04-22T17:10:38.029083904', '2022-04-22T17:10:53.028083968'], dtype='datetime64[ns]') - x(x)float642.001e+05 2.002e+05 ... 2.999e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
array([200092.5, 200212.5, 200332.5, ..., 299692.5, 299812.5, 299932.5])
- y(y)float643.2e+06 3.2e+06 ... 3.1e+06 3.1e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
array([3199867.5, 3199747.5, 3199627.5, ..., 3100267.5, 3100147.5, 3100027.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 401.35 kiB 401.35 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 18.42 MiB 18.42 MiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - mapping()<U1...
- CoordinateAxisTypes :
- GeoX GeoY
- CoordinateTransformType :
- Projection
- GeoTransform :
- 200032.5 120.0 0 3199927.5 0 -120.0
- grid_mapping_name :
- universal_transverse_mercator
- inverse_flattening :
- 298.257223563
- semi_major_axis :
- 6378137.0
- spatial_epsg :
- 32645
- spatial_proj4 :
- +proj=utm +zone=45 +datum=WGS84 +units=m +no_defs
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- utm_zone_number :
- 45.0
array('', dtype='<U1') - mission_img1(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(20549, 40, 40), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 125.42 MiB Shape (20549, 833, 833) (20549, 40, 40) Count 883 Tasks 441 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
When we read in the zarr datacube as a xr.Dataset we set the chunk sizes to auto. When we try to sort along the mid_date dimension this seems to become a problem and we get the warning above.
Let’s follow the instructions in the warning message to avoid creating large chunks:
import dask
with dask.config.set(**{'array.slicing.split_large_chunks': True}):
dc_timesorted = dc.sortby(dc['mid_date'])
dc_timesorted
dc_timesorted
<xarray.Dataset>
Dimensions: (mid_date: 20549, y: 833, x: 833)
Coordinates:
* mid_date (mid_date) datetime64[ns] 2013-04-09T16:49:00....
* x (x) float64 2.001e+05 2.002e+05 ... 2.999e+05
* y (y) float64 3.2e+06 3.2e+06 ... 3.1e+06 3.1e+06
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(20549,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
... ...
vy_error_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_modeled (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 20549
- y: 833
- x: 833
- mid_date(mid_date)datetime64[ns]2013-04-09T16:49:00.528084992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2013-04-09T16:49:00.528084992', '2013-04-23T04:55:55.528083968', '2013-04-25T16:49:09.528084992', ..., '2022-04-20T05:10:57.028083968', '2022-04-22T17:10:38.029083904', '2022-04-22T17:10:53.028083968'], dtype='datetime64[ns]') - x(x)float642.001e+05 2.002e+05 ... 2.999e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
array([200092.5, 200212.5, 200332.5, ..., 299692.5, 299812.5, 299932.5])
- y(y)float643.2e+06 3.2e+06 ... 3.1e+06 3.1e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
array([3199867.5, 3199747.5, 3199627.5, ..., 3100267.5, 3100147.5, 3100027.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 401.35 kiB 401.35 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 18.42 MiB 18.42 MiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - mapping()<U1...
- CoordinateAxisTypes :
- GeoX GeoY
- CoordinateTransformType :
- Projection
- GeoTransform :
- 200032.5 120.0 0 3199927.5 0 -120.0
- grid_mapping_name :
- universal_transverse_mercator
- inverse_flattening :
- 298.257223563
- semi_major_axis :
- 6378137.0
- spatial_epsg :
- 32645
- spatial_proj4 :
- +proj=utm +zone=45 +datum=WGS84 +units=m +no_defs
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- utm_zone_number :
- 45.0
array('', dtype='<U1') - mission_img1(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(49, 40, 40), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 53.12 GiB 306.25 kiB Shape (20549, 833, 833) (49, 40, 40) Count 185662 Tasks 185220 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 4 Tasks 1 Chunks Type float64 numpy.ndarray
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
for element in range(10):
print(dc_timesorted.mid_date[element].data)
2013-04-09T16:49:00.528084992
2013-04-23T04:55:55.528083968
2013-04-25T16:49:09.528084992
2013-05-01T04:56:02.028083968
2013-05-03T16:49:08.028084992
2013-05-09T04:56:00.528083968
2013-05-09T04:56:03.528083968
2013-05-10T04:49:51.028084992
2013-05-17T04:56:02.028083968
2013-05-18T04:49:49.528084992
Read in vector data¶
We are going to read in RGI region 15 (SouthAsiaEast). RGI data is downloaded in lat/lon coordinates. We will project it to match the CRS of the ITS_LIVE dataset and then select an individual glacier to begin our analysis.
se_asia = gpd.read_file('/Users/emarshall/Desktop/siparcs/data/nsidc0770_15.rgi60.SouthAsiaEast/15_rgi60_SouthAsiaEast.shp')
se_asia.head(3)
| RGIId | GLIMSId | BgnDate | EndDate | CenLon | CenLat | O1Region | O2Region | Area | Zmin | ... | Aspect | Lmax | Status | Connect | Form | TermType | Surging | Linkages | Name | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | RGI60-15.00001 | G102044E29941N | 19990920 | -9999999 | 102.044042 | 29.941000 | 15 | 3 | 0.438 | 4996 | ... | 251 | 850 | 0 | 0 | 0 | 0 | 9 | 9 | None | POLYGON ((102.03759 29.93828, 102.03759 29.938... |
| 1 | RGI60-15.00002 | G102042E29987N | 19990920 | -9999999 | 102.042346 | 29.987019 | 15 | 3 | 0.644 | 4947 | ... | 244 | 1021 | 0 | 0 | 0 | 0 | 9 | 9 | None | POLYGON ((102.04195 29.99030, 102.04197 29.990... |
| 2 | RGI60-15.00003 | G102041E29997N | 19990920 | -9999999 | 102.041130 | 29.997311 | 15 | 3 | 0.225 | 5019 | ... | 274 | 812 | 0 | 0 | 0 | 0 | 9 | 9 | None | POLYGON ((102.03710 29.99774, 102.03719 29.998... |
3 rows × 23 columns
#project rgi data to match itslive
se_asia_prj = se_asia.to_crs('EPSG:32645') #we know the epsg from looking at the 'spatial epsg' attr of the mapping var of the dc object
se_asia_prj.head(3)
| RGIId | GLIMSId | BgnDate | EndDate | CenLon | CenLat | O1Region | O2Region | Area | Zmin | ... | Aspect | Lmax | Status | Connect | Form | TermType | Surging | Linkages | Name | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | RGI60-15.00001 | G102044E29941N | 19990920 | -9999999 | 102.044042 | 29.941000 | 15 | 3 | 0.438 | 4996 | ... | 251 | 850 | 0 | 0 | 0 | 0 | 9 | 9 | None | POLYGON ((1959630.570 3408951.748, 1959630.394... |
| 1 | RGI60-15.00002 | G102042E29987N | 19990920 | -9999999 | 102.042346 | 29.987019 | 15 | 3 | 0.644 | 4947 | ... | 244 | 1021 | 0 | 0 | 0 | 0 | 9 | 9 | None | POLYGON ((1959271.126 3414873.173, 1959273.308... |
| 2 | RGI60-15.00003 | G102041E29997N | 19990920 | -9999999 | 102.041130 | 29.997311 | 15 | 3 | 0.225 | 5019 | ... | 274 | 812 | 0 | 0 | 0 | 0 | 9 | 9 | None | POLYGON ((1958682.136 3415647.929, 1958684.710... |
3 rows × 23 columns
Crop RGI to ITS_LIVE extent¶
use get_bbox_single() from access nb but no plotting (above)
#first, get vector bbox of itslive
bbox_dc = get_bbox_single(dc)
bbox_dc['geometry']
#subset rgi to bounds
se_asia_subset = gpd.clip(se_asia_prj, bbox_dc)
se_asia_subset
se_asia_subset.explore()
sample_glacier_vec = se_asia_subset.loc[se_asia_subset['RGIId'] == 'RGI60-15.04714']
sample_glacier_vec
| RGIId | GLIMSId | BgnDate | EndDate | CenLon | CenLat | O1Region | O2Region | Area | Zmin | ... | Aspect | Lmax | Status | Connect | Form | TermType | Surging | Linkages | Name | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4713 | RGI60-15.04714 | G084393E28743N | 20010929 | -9999999 | 84.392609 | 28.743019 | 15 | 1 | 13.593 | 4481 | ... | 317 | 12292 | 0 | 0 | 0 | 0 | 9 | 9 | None | POLYGON ((242056.441 3181919.463, 242008.653 3... |
1 rows × 23 columns
Clip ITS_LIVE dataset to individual glacier extent¶
First, we need to use rio.write_crs() to assign a CRS to the itslive object. If we don’t do that first the rio.clip() command will produce an error
Note: it looks like you can only run write_crs() once, because it switches mapping from being a data_var to a coord so if you run it again it will produce a key error looking for a var that doesnt’ exist
dc_timesorted = dc_timesorted.rio.write_crs(f"epsg:{dc_timesorted.mapping.attrs['spatial_epsg']}", inplace=True)
%%time
sample_glacier_raster = dc_timesorted.rio.clip(sample_glacier_vec.geometry, sample_glacier_vec.crs)
CPU times: user 286 ms, sys: 8.39 ms, total: 294 ms
Wall time: 292 ms
sample_glacier_raster
<xarray.Dataset>
Dimensions: (mid_date: 20549, y: 54, x: 100)
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2013-04-09T16:49:00....
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.479e+05
Data variables: (12/53)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(20549,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
... ...
vy_error_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_modeled (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 20549
- y: 54
- x: 100
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2013-04-09T16:49:00.528084992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2013-04-09T16:49:00.528084992', '2013-04-23T04:55:55.528083968', '2013-04-25T16:49:09.528084992', ..., '2022-04-20T05:10:57.028083968', '2022-04-22T17:10:38.029083904', '2022-04-22T17:10:53.028083968'], dtype='datetime64[ns]') - y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 401.35 kiB 401.35 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 18.42 MiB 18.42 MiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - mission_img1(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
Let’s take a look at the clipped raster alongside the vector outline. To start with and for the sake of easy visualizing we will take the mean of the magnitude of velocity variable along the mid_date dimension:
fig, ax = plt.subplots(figsize = (15,9))
sample_glacier_vec.plot(ax=ax, facecolor='none', edgecolor='red');
sample_glacier_raster.v.mean(dim=['mid_date']).plot(ax=ax);
Now let’s take a look at the x and y components of velocity, again averaging over time:
fig, axs = plt.subplots(ncols =2, figsize=(17,7))
sample_glacier_raster.vx.mean(dim='mid_date').plot(ax=axs[0])
sample_glacier_raster.vy.mean(dim='mid_date').plot(ax=axs[1])
<matplotlib.collections.QuadMesh at 0x1910d7ca0>
sample_glacier_raster.v_error.mean(dim=['mid_date']).plot()
<matplotlib.collections.QuadMesh at 0x1b39afac0>
Exploring ITS_LIVE data¶
ITS_LIVE data cubes come with many (53!) variables that carry information about the estimated surface velocities and the satellite images that were used to generate the surface velocity estimates. We won’t examine all of this information here but let’s look at a litte bit.
To start with, let’s look at the satellite imagery used to generate the velocity data.
We see that we have two data_vars that indicate which sensor that each image in the image pair at a certain time step comes from:
sample_glacier_raster.satellite_img1.data.compute()
array(['8.', '8.', '8.', ..., '2A', '2B', '2B'], dtype='<U2')
sample_glacier_raster.satellite_img2
<xarray.DataArray 'satellite_img2' (mid_date: 20549)>
dask.array<copy, shape=(20549,), dtype=<U2, chunksize=(20549,), chunktype=numpy.ndarray>
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2013-04-09T16:49:00.528084992 ... 202...
Attributes:
description: id of the satellite that acquired image 2
standard_name: image2_satellite- mid_date: 20549
- dask.array<chunksize=(20549,), meta=np.ndarray>
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2013-04-09T16:49:00.528084992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2013-04-09T16:49:00.528084992', '2013-04-23T04:55:55.528083968', '2013-04-25T16:49:09.528084992', ..., '2022-04-20T05:10:57.028083968', '2022-04-22T17:10:38.029083904', '2022-04-22T17:10:53.028083968'], dtype='datetime64[ns]')
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
The satellite_img1 and satellite_img2 variables are 1-dimensional numpy arrays corresponding to the length of the mid_date dimension of the data cube. You can see that each element of the array is a string corresponding to a different satellite:
1A = Sentinel 1A, 1B = Sentinel 1B, 2A = Sentinel 2A
2B = Sentinel 2B, 8. = Landsat8 and 9. = Landsat9
Let’s re-arrange these string arrays into a format that is easier to work with.
First, we’ll make a set of all the different string values in the satellite image variables:
sat_ls1 = list(set(sample_glacier_raster.satellite_img1.compute().data)) #these should be the same, and img1 img2 should only
sat_ls2 = set(sample_glacier_raster.satellite_img2.compute().data) #differ if its someting like 2a, 2b or 1a, 1b so i think shouldn't
#have to worry about the 2 vars ?
Next, we’ll assign a value to each element in the set:
mapping = {}
for x in range(len(sat_ls1)):
mapping[sat_ls1[x]] = x
print('mapping: ', mapping)
print('')
mapping: {'2A': 0, '2B': 1, '1A': 2, '9.': 3, '8.': 4, '1B': 5}
We’ll then convert each element of the satellite image variable arrays to a binary array that gives us the integer associated with each sensor:
#convert each satellite_img1 value to binary array indicated int associated with sensor
one_hot_encode = []
for c in sample_glacier_raster.satellite_img1.compute().data:
arr = list(np.zeros(len(sat_ls1), dtype=int))
arr[mapping[c]]= 1
one_hot_encode.append(arr)
Back out the sensor integer from the binary array:
sensor_ints = [int(one_hot_encode[x].index(1)) for x in range(len(one_hot_encode))]
Then make a pandas dataframe with each mid_date of the data cube and the sensor integer
dates_ls = list(sample_glacier_raster.mid_date.data)
#make dataframe of sensor ints and associated img date
sat_df = pd.DataFrame({'mid_date1':dates_ls, 'sensor': sensor_ints})
sat_df['mid_date'] = sat_df['mid_date1'].dt.date
sat_df = sat_df.drop('mid_date1', axis=1)
sat_df = sat_df.sort_values(by='mid_date')
sat_df = sat_df.set_index('mid_date')
As a first step, let’s visualize the time series of different sensors as a heat map
#make heatmap
pal = sns.color_palette('Paired',6)
fig, ax = plt.subplots(figsize=(20,4))
sns.heatmap(sat_df.T, cmap=pal, ax=ax);
We can wrap those steps into a function to use them more easily:
#help from: https://www.educative.io/answers/one-hot-encoding-in-python
#help from: https://datascienceparichay.com/article/remove-time-from-date-pandas/
def get_satellite_as_int(input_da):
''' Function that takes a dast xr.DataArray that represents what sensor velocity data from a specific date was collected from.
returns an xr.DataArray of the sensor coded as an integer key as well as a pandas df with mid_date as index, sensor integer as
a column. **still need to figure out how to carry the mapping of what sensor str corresponds to what sensor integer through**'''
#make list of satellite strs
sat_ls = list(set(input_da.compute().data))
#sat_ls = list(set(input_da.data))
#map strs to ints
mapping = {}
for x in range(len(sat_ls)):
mapping[sat_ls[x]] = x
print('mapping: ', mapping)
#convert each satellite_img1 value to binary array indicated int associated with sensor
one_hot_encode = []
for c in input_da.compute().data:
arr = list(np.zeros(len(sat_ls), dtype=int))
arr[mapping[c]]= 1
one_hot_encode.append(arr)
sensor_ints = [one_hot_encode[x].index(1) for x in range(len(one_hot_encode))]
dates_ls = list(input_da.mid_date.data)
#make dataframe of sensor ints and associated img date
sat_df = pd.DataFrame({'mid_date1':dates_ls, 'sensor': sensor_ints})
#sat_df['mid_date'] = sat_df['mid_date1'].dt.date
#sat_df = sat_df.drop('mid_date1', axis=1)
sat_df = sat_df.sort_values(by='mid_date1')
sat_df = sat_df.set_index('mid_date1')
sat_xr = sat_df['sensor'].to_xarray()
return sat_xr, sat_df
a = get_satellite_as_int(sample_glacier_raster.satellite_img1.compute())[0]
mapping: {'2A': 0, '2B': 1, '1A': 2, '9.': 3, '8.': 4, '1B': 5}
And if we want we can add this new xarray.DataArray back as a data_var in the original xarray.Dataset:
sample_glacier_raster['satellite_img_int'] = ('mid_date', a.data)
sample_glacier_raster
<xarray.Dataset>
Dimensions: (mid_date: 20549, y: 54, x: 100)
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2013-04-09T16:49:00....
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.479e+05
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(20549,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(20549,), meta=np.ndarray>
... ...
vy_error_modeled (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(20549,), meta=np.ndarray>
satellite_img_int (mid_date) int64 4 4 4 4 4 4 4 ... 1 0 1 0 0 1 1
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 20549
- y: 54
- x: 100
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2013-04-09T16:49:00.528084992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2013-04-09T16:49:00.528084992', '2013-04-23T04:55:55.528083968', '2013-04-25T16:49:09.528084992', ..., '2022-04-20T05:10:57.028083968', '2022-04-22T17:10:38.029083904', '2022-04-22T17:10:53.028083968'], dtype='datetime64[ns]') - y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 401.35 kiB 401.35 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 18.42 MiB 18.42 MiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - mission_img1(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 80.27 kiB 80.27 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 240.81 kiB 240.81 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 423.30 MiB 260.31 kiB Shape (20549, 54, 100) (49, 34, 40) Count 374285 Tasks 2520 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(20549,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 160.54 kiB 160.54 kiB Shape (20549,) (20549,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img_int(mid_date)int644 4 4 4 4 4 4 4 ... 0 1 0 1 0 0 1 1
array([4, 4, 4, ..., 0, 1, 1])
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
Examining velocity data from each satellite in ITS_LIVE dataset¶
What if we only wanted to look at the velocity estimates from landat8?
l8_data = sample_glacier_raster.where(sample_glacier_raster['satellite_img_int'] == 5., drop=True)
l8_data
<xarray.Dataset>
Dimensions: (mid_date: 27, y: 54, x: 100)
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2016-10-17T00:18:51....
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.479e+05
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(27,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(27,), meta=np.ndarray>
autoRIFT_software_version (mid_date) object dask.array<chunksize=(27,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(27,), meta=np.ndarray>
... ...
vy_error_modeled (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
satellite_img_int (mid_date) float64 5.0 5.0 5.0 ... 5.0 5.0 5.0
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 27
- y: 54
- x: 100
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2016-10-17T00:18:51.031124992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2016-10-17T00:18:51.031124992', '2016-11-10T00:18:50.986718976', '2016-12-04T00:18:50.407746816', '2016-12-28T00:18:49.616931072', '2017-01-21T00:18:47.713193984', '2019-10-02T00:19:29.781902080', '2019-10-05T00:19:10.463773952', '2019-10-14T00:19:29.688044032', '2019-10-17T00:19:10.503399936', '2019-10-26T00:19:29.804210944', '2019-10-29T00:19:10.492844032', '2019-11-07T00:19:29.726312960', '2019-11-10T00:19:10.422557952', '2019-11-19T00:19:29.612967936', '2019-11-22T00:19:06.076982016', '2019-12-01T00:19:24.958162176', '2019-12-04T00:19:01.536876288', '2019-12-13T00:19:24.628526848', '2019-12-16T00:19:01.126296064', '2019-12-25T00:19:24.136749056', '2019-12-28T00:19:00.552694272', '2020-01-06T00:19:23.571698944', '2020-01-09T00:18:59.955597056', '2020-01-18T00:19:23.074541056', '2020-07-19T00:19:10.073351936', '2020-07-20T12:22:09.336307968', '2020-07-28T00:19:20.356583936'], dtype='datetime64[ns]') - y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - mission_img1(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - mission_img2(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - satellite_img2(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - sensor_img1(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - sensor_img2(mid_date)objectdask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type object numpy.ndarray - stable_count_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type float64 numpy.ndarray - stable_count_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type float64 numpy.ndarray - stable_shift_flag(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 10 Tasks 1 Chunks Type float64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374566 Tasks 120 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 9 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img_int(mid_date)float645.0 5.0 5.0 5.0 ... 5.0 5.0 5.0 5.0
array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
dataset.where() at first seems appropriate to use for kind of operation but there’s actually an easier way. Because we are selecting along a single dimension (mid_date), we can use xarray’s .sel() method instead. This is more efficient and integrates with dask arrays more smoothly.
l8_condition = sample_glacier_raster.satellite_img_int.isin(5.)
l8_subset = sample_glacier_raster.sel(mid_date=l8_condition)
l8_subset
<xarray.Dataset>
Dimensions: (mid_date: 27, y: 54, x: 100)
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2016-10-17T00:18:51....
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.479e+05
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(27,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(27,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(27,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(27,), meta=np.ndarray>
... ...
vy_error_modeled (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(27,), meta=np.ndarray>
satellite_img_int (mid_date) int64 5 5 5 5 5 5 5 ... 5 5 5 5 5 5 5
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 27
- y: 54
- x: 100
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2016-10-17T00:18:51.031124992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2016-10-17T00:18:51.031124992', '2016-11-10T00:18:50.986718976', '2016-12-04T00:18:50.407746816', '2016-12-28T00:18:49.616931072', '2017-01-21T00:18:47.713193984', '2019-10-02T00:19:29.781902080', '2019-10-05T00:19:10.463773952', '2019-10-14T00:19:29.688044032', '2019-10-17T00:19:10.503399936', '2019-10-26T00:19:29.804210944', '2019-10-29T00:19:10.492844032', '2019-11-07T00:19:29.726312960', '2019-11-10T00:19:10.422557952', '2019-11-19T00:19:29.612967936', '2019-11-22T00:19:06.076982016', '2019-12-01T00:19:24.958162176', '2019-12-04T00:19:01.536876288', '2019-12-13T00:19:24.628526848', '2019-12-16T00:19:01.126296064', '2019-12-25T00:19:24.136749056', '2019-12-28T00:19:00.552694272', '2020-01-06T00:19:23.571698944', '2020-01-09T00:18:59.955597056', '2020-01-18T00:19:23.074541056', '2020-07-19T00:19:10.073351936', '2020-07-20T12:22:09.336307968', '2020-07-28T00:19:20.356583936'], dtype='datetime64[ns]') - y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 540 B 540 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 24.79 kiB 24.79 kiB Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - mission_img1(mid_date)<U1dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 108 B 108 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 108 B 108 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 324 B 324 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 324 B 324 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 569.53 kiB 10.62 kiB Shape (27, 54, 100) (2, 34, 40) Count 374405 Tasks 120 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(27,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img_int(mid_date)int645 5 5 5 5 5 5 5 ... 5 5 5 5 5 5 5 5
array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5])
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
We can see that we are looking at roughly a third of the original time steps. Let’s take a look at the average speeds of the Landsat8-derived velocities:
l8_subset.v.mean(dim='mid_date').plot();
What about Landsat9?
l9_condition = sample_glacier_raster.satellite_img_int.isin(1.)
l9_subset = sample_glacier_raster.sel(mid_date=l9_condition)
l9_subset
<xarray.Dataset>
Dimensions: (mid_date: 6677, y: 54, x: 100)
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2017-09-23T05:03:02....
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.479e+05
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(6677,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(6677,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(6677,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(6677,), meta=np.ndarray>
... ...
vy_error_modeled (mid_date) float64 dask.array<chunksize=(6677,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(6677,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(6677,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(6677,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(6677,), meta=np.ndarray>
satellite_img_int (mid_date) int64 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 6677
- y: 54
- x: 100
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2017-09-23T05:03:02.028083968 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2017-09-23T05:03:02.028083968', '2017-09-25T17:02:58.028083968', '2017-10-03T05:04:08.028083968', ..., '2022-04-17T17:10:53.528083968', '2022-04-22T17:10:38.029083904', '2022-04-22T17:10:53.028083968'], dtype='datetime64[ns]') - y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 130.41 kiB 130.41 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 5.99 MiB 5.99 MiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - mission_img1(mid_date)<U1dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 26.08 kiB 26.08 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 26.08 kiB 26.08 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 78.25 kiB 78.25 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 78.25 kiB 78.25 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 137.54 MiB 159.38 kiB Shape (6677, 54, 100) (30, 34, 40) Count 376283 Tasks 1998 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(6677,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 52.16 kiB 52.16 kiB Shape (6677,) (6677,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img_int(mid_date)int641 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
array([1, 1, 1, ..., 1, 1, 1])
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
Only 45 time steps have data from Landsat9, this makes sense because Landsat9 was just launched recently
l9_subset.v.mean(dim='mid_date').plot();
Let’s look at Sentinel 1 data. Note here we are selecting for 2 values instead of 1:
s1_condition = sample_glacier_raster.satellite_img_int.isin([0,2])
s1_subset = sample_glacier_raster.sel(mid_date = s1_condition)
s1_subset
<xarray.Dataset>
Dimensions: (mid_date: 6542, y: 54, x: 100)
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2014-10-13T00:19:06....
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.479e+05
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(6542,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(6542,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(6542,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(6542,), meta=np.ndarray>
... ...
vy_error_modeled (mid_date) float64 dask.array<chunksize=(6542,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(6542,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(6542,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(6542,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(6542,), meta=np.ndarray>
satellite_img_int (mid_date) int64 2 2 2 2 2 2 2 ... 0 0 0 0 0 0 0
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 6542
- y: 54
- x: 100
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2014-10-13T00:19:06.329022976 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2014-10-13T00:19:06.329022976', '2014-10-25T00:19:06.133405952', '2014-11-06T00:19:05.893705984', ..., '2022-04-17T17:10:52.028083968', '2022-04-20T05:10:42.029083904', '2022-04-20T05:10:57.028083968'], dtype='datetime64[ns]') - y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 127.77 kiB 127.77 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 5.86 MiB 5.86 MiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - mission_img1(mid_date)<U1dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 25.55 kiB 25.55 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 25.55 kiB 25.55 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 76.66 kiB 76.66 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 76.66 kiB 76.66 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(2, 20, 21), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 134.76 MiB 170.00 kiB Shape (6542, 54, 100) (32, 34, 40) Count 376637 Tasks 2352 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(6542,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 51.11 kiB 51.11 kiB Shape (6542,) (6542,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img_int(mid_date)int642 2 2 2 2 2 2 2 ... 0 0 0 0 0 0 0 0
array([2, 2, 2, ..., 0, 0, 0])
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
s1_subset.v.mean(dim='mid_date').plot();
s2_condition = sample_glacier_raster.satellite_img_int.isin([3,4])
s2_subset = sample_glacier_raster.sel(mid_date=s2_condition)
s2_subset
<xarray.Dataset>
Dimensions: (mid_date: 7303, y: 54, x: 100)
Coordinates:
mapping int64 0
* mid_date (mid_date) datetime64[ns] 2013-04-09T16:49:00....
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.479e+05
Data variables: (12/54)
acquisition_date_img1 (mid_date) datetime64[ns] dask.array<chunksize=(7303,), meta=np.ndarray>
acquisition_date_img2 (mid_date) datetime64[ns] dask.array<chunksize=(7303,), meta=np.ndarray>
autoRIFT_software_version (mid_date) <U5 dask.array<chunksize=(7303,), meta=np.ndarray>
chip_size_height (mid_date, y, x) float32 dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
chip_size_width (mid_date, y, x) float32 dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
date_center (mid_date) datetime64[ns] dask.array<chunksize=(7303,), meta=np.ndarray>
... ...
vy_error_modeled (mid_date) float64 dask.array<chunksize=(7303,), meta=np.ndarray>
vy_error_slow (mid_date) float64 dask.array<chunksize=(7303,), meta=np.ndarray>
vy_stable_shift (mid_date) float64 dask.array<chunksize=(7303,), meta=np.ndarray>
vy_stable_shift_mask (mid_date) float64 dask.array<chunksize=(7303,), meta=np.ndarray>
vy_stable_shift_slow (mid_date) float64 dask.array<chunksize=(7303,), meta=np.ndarray>
satellite_img_int (mid_date) int64 4 4 4 4 4 4 4 ... 3 4 3 3 4 3 4
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- mid_date: 7303
- y: 54
- x: 100
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- mid_date(mid_date)datetime64[ns]2013-04-09T16:49:00.528084992 .....
- description :
- midpoint of image 1 and image 2 acquisition date and time with granule's centroid longitude and latitude as microseconds
- standard_name :
- image_pair_center_date_with_time_separation
array(['2013-04-09T16:49:00.528084992', '2013-04-23T04:55:55.528083968', '2013-04-25T16:49:09.528084992', ..., '2022-01-30T04:54:20.986952960', '2022-02-03T04:54:22.952357120', '2022-02-07T04:54:18.845935104'], dtype='datetime64[ns]') - y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5])
- acquisition_date_img1(mid_date)datetime64[ns]dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- acquisition date and time of image 1
- standard_name :
- image1_acquition_date
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - acquisition_date_img2(mid_date)datetime64[ns]dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- acquisition date and time of image 2
- standard_name :
- image2_acquition_date
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - autoRIFT_software_version(mid_date)<U5dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- version of autoRIFT software
- standard_name :
- autoRIFT_software_version
Array Chunk Bytes 142.64 kiB 142.64 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - chip_size_height(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- height of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_height
- units :
- m
- y_pixel_size :
- 10.0
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - chip_size_width(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- chip_size_coordinates :
- Optical data: chip_size_coordinates = 'image projection geometry: width = x, height = y'. Radar data: chip_size_coordinates = 'radar geometry: width = range, height = azimuth'
- description :
- width of search template (chip)
- grid_mapping :
- mapping
- standard_name :
- chip_size_width
- units :
- m
- x_pixel_size :
- 10.0
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - date_center(mid_date)datetime64[ns]dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- midpoint of image 1 and image 2 acquisition date
- standard_name :
- image_pair_center_date
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type datetime64[ns] numpy.ndarray - date_dt(mid_date)timedelta64[ns]dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- time separation between acquisition of image 1 and image 2
- standard_name :
- image_pair_time_separation
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type timedelta64[ns] numpy.ndarray - granule_url(mid_date)<U235dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- original granule URL
- standard_name :
- granule_url
Array Chunk Bytes 6.55 MiB 6.55 MiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - interp_mask(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- light interpolation mask
- grid_mapping :
- mapping
- standard_name :
- interpolated_value_mask
- units :
- binary
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - mission_img1(mid_date)<U1dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- id of the mission that acquired image 1
- standard_name :
- image1_mission
Array Chunk Bytes 28.53 kiB 28.53 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - mission_img2(mid_date)<U1dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- id of the mission that acquired image 2
- standard_name :
- image2_mission
Array Chunk Bytes 28.53 kiB 28.53 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - roi_valid_percentage(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- percentage of pixels with a valid velocity estimate determined for the intersection of the full image pair footprint and the region of interest (roi) that defines where autoRIFT tried to estimate a velocity
- standard_name :
- region_of_interest_valid_pixel_percentage
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img1(mid_date)<U2dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 1
- standard_name :
- image1_satellite
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - satellite_img2(mid_date)<U2dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- id of the satellite that acquired image 2
- standard_name :
- image2_satellite
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img1(mid_date)<U3dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 1
- standard_name :
- image1_sensor
Array Chunk Bytes 85.58 kiB 85.58 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - sensor_img2(mid_date)<U3dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- id of the sensor that acquired image 2
- standard_name :
- image2_sensor
Array Chunk Bytes 85.58 kiB 85.58 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type numpy.ndarray - stable_count_mask(mid_date)int64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- number of valid pixels over stationary or slow-flowing surfaces
- standard_name :
- stable_count_mask
- units :
- count
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_count_slow(mid_date)int64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- number of valid pixels over slowest 25% of ice
- standard_name :
- stable_count_slow
- units :
- count
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - stable_shift_flag(mid_date)int64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- flag for applying velocity bias correction: 0 = no correction; 1 = correction from overlapping stable surface mask (stationary or slow-flowing surfaces with velocity < 15 m/yr)(top priority); 2 = correction from slowest 25% of overlapping velocities (second priority)
- standard_name :
- stable_shift_flag
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type int64 numpy.ndarray - v(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude
- grid_mapping :
- mapping
- standard_name :
- velocity
- units :
- m/y
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - v_error(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity magnitude error
- grid_mapping :
- mapping
- standard_name :
- velocity_error
- units :
- m/y
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - va(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity in radar azimuth direction
- grid_mapping :
- mapping
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - va_error(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- error for velocity in radar azimuth direction
- standard_name :
- va_error
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_error_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_modeled(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- va_error_modeled
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_error_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- va_error_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- applied va shift calibrated using pixels over stable or slow surfaces
- standard_name :
- va_stable_shift
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- va_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- va shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- va_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity in radar range direction
- grid_mapping :
- mapping
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - vr_error(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- error for velocity in radar range direction
- standard_name :
- vr_error
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_error_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_modeled(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vr_error_modeled
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_error_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vr_error_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- applied vr shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vr_stable_shift
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vr_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- vr shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vr_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity component in x direction
- grid_mapping :
- mapping
- standard_name :
- x_velocity
- units :
- m/y
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - vx_error(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- best estimate of x_velocity error: vx_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vx_error
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_error_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_modeled(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vx_error_modeled
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_error_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vx_error_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- applied vx shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vx_stable_shift
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vx_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- vx shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vx_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy(mid_date, y, x)float32dask.array<chunksize=(49, 20, 21), meta=np.ndarray>
- description :
- velocity component in y direction
- grid_mapping :
- mapping
- standard_name :
- y_velocity
- units :
- m/y
Array Chunk Bytes 150.44 MiB 260.31 kiB Shape (7303, 54, 100) (49, 34, 40) Count 376649 Tasks 2364 Chunks Type float32 numpy.ndarray - vy_error(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- best estimate of y_velocity error: vy_error is populated according to the approach used for the velocity bias correction as indicated in "stable_shift_flag"
- standard_name :
- vy_error
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_error_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_modeled(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- 1-sigma error calculated using a modeled error-dt relationship
- standard_name :
- vy_error_modeled
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_error_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- RMSE over slowest 25% of retrieved velocities
- standard_name :
- vy_error_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- applied vy shift calibrated using pixels over stable or slow surfaces
- standard_name :
- vy_stable_shift
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over stable surfaces, stationary or slow-flowing surfaces with velocity < 15 m/yr identified from an external mask
- standard_name :
- vy_stable_shift_mask
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(mid_date)float64dask.array<chunksize=(7303,), meta=np.ndarray>
- description :
- vy shift calibrated using valid pixels over slowest 25% of retrieved velocities
- standard_name :
- vy_stable_shift_slow
- units :
- m/y
Array Chunk Bytes 57.05 kiB 57.05 kiB Shape (7303,) (7303,) Count 6 Tasks 1 Chunks Type float64 numpy.ndarray - satellite_img_int(mid_date)int644 4 4 4 4 4 4 4 ... 4 3 4 3 3 4 3 4
array([4, 4, 4, ..., 4, 3, 4])
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
s2_subset.v.mean(dim='mid_date').plot();
#attempt at ufunc, didn't work
#def xr_satellite_coding(a):
# return xr.apply_ufunc(get_satellite_as_int, a)
#sample_glacier_raster
#test_ds = xr_satellite_coding(sample_glacier_raster.satellite_img1)
pal = sns.color_palette('Paired',6)
pal
Seasonal mean velocities with groupby¶
#first define the function we'll apply to each group
def middate_mean(a):
return a.mean(dim='mid_date')
seasons_gb = sample_glacier_raster.groupby(sample_glacier_raster.mid_date.dt.season).map(middate_mean)
#add attrs to gb object
seasons_gb.attrs = sample_glacier_raster.attrs #why didn't that work?
seasons_gb
<xarray.Dataset>
Dimensions: (y: 54, x: 100, season: 4)
Coordinates:
mapping int64 0
* y (y) float64 3.188e+06 3.188e+06 ... 3.182e+06
* x (x) float64 2.36e+05 2.361e+05 ... 2.477e+05 2.479e+05
* season (season) object 'DJF' 'JJA' 'MAM' 'SON'
Data variables: (12/43)
chip_size_height (season, y, x) float32 dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
chip_size_width (season, y, x) float32 dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
date_dt (season) timedelta64[ns] dask.array<chunksize=(1,), meta=np.ndarray>
interp_mask (season, y, x) float32 dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
roi_valid_percentage (season) float64 dask.array<chunksize=(1,), meta=np.ndarray>
stable_count_mask (season) float64 dask.array<chunksize=(1,), meta=np.ndarray>
... ...
vy_error_modeled (season) float64 dask.array<chunksize=(1,), meta=np.ndarray>
vy_error_slow (season) float64 dask.array<chunksize=(1,), meta=np.ndarray>
vy_stable_shift (season) float64 dask.array<chunksize=(1,), meta=np.ndarray>
vy_stable_shift_mask (season) float64 dask.array<chunksize=(1,), meta=np.ndarray>
vy_stable_shift_slow (season) float64 dask.array<chunksize=(1,), meta=np.ndarray>
satellite_img_int (season) float64 1.7 1.793 1.894 1.806
Attributes: (12/18)
GDAL_AREA_OR_POINT: Area
author: ITS_LIVE, a NASA MEaSUREs project (its-live.j...
autoRIFT_parameter_file: http://its-live-data.s3.amazonaws.com/autorif...
datacube_software_version: 1.0
date_created: 09-Jun-2022 04:14:40
date_updated: 09-Jun-2022 04:14:40
... ...
s3: s3://its-live-data/datacubes/v02/N20E080/ITS_...
skipped_granules: s3://its-live-data/datacubes/v02/N20E080/ITS_...
time_standard_img1: UTC
time_standard_img2: UTC
title: ITS_LIVE datacube of image_pair velocities
url: https://its-live-data.s3.amazonaws.com/datacu...- y: 54
- x: 100
- season: 4
- mapping()int640
- crs_wkt :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.314245179
- inverse_flattening :
- 298.257223563
- reference_ellipsoid_name :
- WGS 84
- longitude_of_prime_meridian :
- 0.0
- prime_meridian_name :
- Greenwich
- geographic_crs_name :
- WGS 84
- horizontal_datum_name :
- World Geodetic System 1984
- projected_crs_name :
- WGS 84 / UTM zone 45N
- grid_mapping_name :
- transverse_mercator
- latitude_of_projection_origin :
- 0.0
- longitude_of_central_meridian :
- 87.0
- false_easting :
- 500000.0
- false_northing :
- 0.0
- scale_factor_at_central_meridian :
- 0.9996
- spatial_ref :
- PROJCS["WGS 84 / UTM zone 45N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",87],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32645"]]
array(0)
- y(y)float643.188e+06 3.188e+06 ... 3.182e+06
- description :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- axis :
- Y
- long_name :
- y coordinate of projection
- units :
- metre
array([3187867.5, 3187747.5, 3187627.5, 3187507.5, 3187387.5, 3187267.5, 3187147.5, 3187027.5, 3186907.5, 3186787.5, 3186667.5, 3186547.5, 3186427.5, 3186307.5, 3186187.5, 3186067.5, 3185947.5, 3185827.5, 3185707.5, 3185587.5, 3185467.5, 3185347.5, 3185227.5, 3185107.5, 3184987.5, 3184867.5, 3184747.5, 3184627.5, 3184507.5, 3184387.5, 3184267.5, 3184147.5, 3184027.5, 3183907.5, 3183787.5, 3183667.5, 3183547.5, 3183427.5, 3183307.5, 3183187.5, 3183067.5, 3182947.5, 3182827.5, 3182707.5, 3182587.5, 3182467.5, 3182347.5, 3182227.5, 3182107.5, 3181987.5, 3181867.5, 3181747.5, 3181627.5, 3181507.5]) - x(x)float642.36e+05 2.361e+05 ... 2.479e+05
- description :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- axis :
- X
- long_name :
- x coordinate of projection
- units :
- metre
array([235972.5, 236092.5, 236212.5, 236332.5, 236452.5, 236572.5, 236692.5, 236812.5, 236932.5, 237052.5, 237172.5, 237292.5, 237412.5, 237532.5, 237652.5, 237772.5, 237892.5, 238012.5, 238132.5, 238252.5, 238372.5, 238492.5, 238612.5, 238732.5, 238852.5, 238972.5, 239092.5, 239212.5, 239332.5, 239452.5, 239572.5, 239692.5, 239812.5, 239932.5, 240052.5, 240172.5, 240292.5, 240412.5, 240532.5, 240652.5, 240772.5, 240892.5, 241012.5, 241132.5, 241252.5, 241372.5, 241492.5, 241612.5, 241732.5, 241852.5, 241972.5, 242092.5, 242212.5, 242332.5, 242452.5, 242572.5, 242692.5, 242812.5, 242932.5, 243052.5, 243172.5, 243292.5, 243412.5, 243532.5, 243652.5, 243772.5, 243892.5, 244012.5, 244132.5, 244252.5, 244372.5, 244492.5, 244612.5, 244732.5, 244852.5, 244972.5, 245092.5, 245212.5, 245332.5, 245452.5, 245572.5, 245692.5, 245812.5, 245932.5, 246052.5, 246172.5, 246292.5, 246412.5, 246532.5, 246652.5, 246772.5, 246892.5, 247012.5, 247132.5, 247252.5, 247372.5, 247492.5, 247612.5, 247732.5, 247852.5]) - season(season)object'DJF' 'JJA' 'MAM' 'SON'
array(['DJF', 'JJA', 'MAM', 'SON'], dtype=object)
- chip_size_height(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - chip_size_width(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - date_dt(season)timedelta64[ns]dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 73 Tasks 4 Chunks Type timedelta64[ns] numpy.ndarray - interp_mask(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - roi_valid_percentage(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - stable_count_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - stable_count_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - stable_shift_flag(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - v(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - v_error(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - va(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - va_error(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - va_error_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - va_error_modeled(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - va_error_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - va_stable_shift(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - va_stable_shift_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - va_stable_shift_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vr(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - vr_error(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vr_error_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vr_error_modeled(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vr_error_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vr_stable_shift(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vr_stable_shift_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vr_stable_shift_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vx(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - vx_error(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vx_error_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vx_error_modeled(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vx_error_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vx_stable_shift(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vx_stable_shift_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vx_stable_shift_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vy(season, y, x)float32dask.array<chunksize=(1, 20, 21), meta=np.ndarray>
Array Chunk Bytes 84.38 kiB 5.31 kiB Shape (4, 54, 100) (1, 34, 40) Count 380723 Tasks 24 Chunks Type float32 numpy.ndarray - vy_error(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vy_error_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vy_error_modeled(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vy_error_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vy_stable_shift(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vy_stable_shift_mask(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - vy_stable_shift_slow(season)float64dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 32 B 8 B Shape (4,) (1,) Count 25 Tasks 4 Chunks Type float64 numpy.ndarray - satellite_img_int(season)float641.7 1.793 1.894 1.806
array([1.700368 , 1.79254271, 1.89397372, 1.80630631])
- GDAL_AREA_OR_POINT :
- Area
- author :
- ITS_LIVE, a NASA MEaSUREs project (its-live.jpl.nasa.gov)
- autoRIFT_parameter_file :
- http://its-live-data.s3.amazonaws.com/autorift_parameters/v001/autorift_landice_0120m.shp
- datacube_software_version :
- 1.0
- date_created :
- 09-Jun-2022 04:14:40
- date_updated :
- 09-Jun-2022 04:14:40
- geo_polygon :
- [[83.94987110174335, 27.991402051034683], [84.20379020129273, 27.996804232037185], [84.45777661714104, 28.001738278786444], [84.71182426076075, 28.00620374995722], [84.96592703474772, 28.01020024594077], [84.96166371314177, 28.235735560542437], [84.95735092626452, 28.46126283461518], [84.9529882277544, 28.686782021725538], [84.94857516388325, 28.912293075803735], [84.69230915033022, 28.90814426439164], [84.43610029091587, 28.903508617931795], [84.17995490589726, 28.898386555855694], [83.92387930602281, 28.892778541374547], [83.9304896768754, 28.66744768772876], [83.9370246495942, 28.44210795053903], [83.9434849022056, 28.216759386124536], [83.94987110174335, 27.991402051034683]]
- institution :
- NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
- latitude :
- 28.45
- longitude :
- 84.45
- proj_polygon :
- [[200000, 3100000], [225000.0, 3100000.0], [250000.0, 3100000.0], [275000.0, 3100000.0], [300000, 3100000], [300000.0, 3125000.0], [300000.0, 3150000.0], [300000.0, 3175000.0], [300000, 3200000], [275000.0, 3200000.0], [250000.0, 3200000.0], [225000.0, 3200000.0], [200000, 3200000], [200000.0, 3175000.0], [200000.0, 3150000.0], [200000.0, 3125000.0], [200000, 3100000]]
- projection :
- 32645
- s3 :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
- skipped_granules :
- s3://its-live-data/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.json
- time_standard_img1 :
- UTC
- time_standard_img2 :
- UTC
- title :
- ITS_LIVE datacube of image_pair velocities
- url :
- https://its-live-data.s3.amazonaws.com/datacubes/v02/N20E080/ITS_LIVE_vel_EPSG32645_G0120_X250000_Y3150000.zarr
fg = seasons_gb.v.plot(
col='season',
vmax = 150
);